edheltzel/Recall

<p align="center"> <img src="assets/banner.png" alt="Recall — Persistent Memory for Coding Agents" width="100%"> </p>

TL;DR

Recall is a retrieval-first memory layer: everything lands in one searchable database, the best of it is ranked and injected at session start, and decisions carry confidence, importance, and a lifecycle across any coding agent/harness.

A SQLite-backed persistent memory layer for coding agents. Stop-hook extraction captures sessions as you work, MCP tools expose them mid-session, hybrid search (FTS5 + embeddings) retrieves them, and a tiered L0/L1 recall block injects identity + top-ranked records at every session start. Works across Claude Code, OpenCode, and Pi from one local database.

Got questions about the project? I'd suggest using DeepWiki from Devin/Cognition to ask questions about the project.

Recall — Persistent Memory for Any Agent Harness

All coding agents forget when a session ends. Recall doesn't — it extracts, indexes, and recalls what matters across every session, across every agent you use.

Built on the Model Context Protocol. One SQLite file. No phone-home. No vendor lock-in.

Stable on Claude Code. Beta on Pi and Alpha for OpenCode (MCP works; lifecycle extensions are early). Codex CLI and Gemini CLI on the roadmap. See Roadmap.

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The Problem

AI agents have no memory between sessions. Context is lost. You repeat yourself. Decisions made last week are forgotten today. Every new session re-learns the basics.

How Recall Fixes It

Install once, then forget about it. Recall runs silently in the background:

┌──────────┐    ┌────────────────┐    ┌──────────────┐    ┌───────────────┐    ┌──────────────┐
│ You Work │─